Publication Date
2021
Document Type
Thesis
Committee Members
Michael L. Raymer, Ph.D. (Advisor); Mateen M. Rizki, Ph.D. (Committee Member); Travis E. Doom, Ph.D. (Committee Member); Thomas Wischgoll, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Scientific collaboration between researchers is very common and much influential and ground-breaking research is performed by teams comprised of scientist from different fields and organizations. In this thesis, we analyze and model a small scientific collaboration network limited to two organizations: Wright State University and the Air Force Research Laboratory. Research paper co-authorship is used for establishing the network structure. We analyze several network properties and compare them to past results from analysis of larger and more diverse collaboration networks. We show that the two-organization network we explored exhibits properties similar to those of larger networks. Guided by advances in state-of-the-art algorithms for the link prediction problem in large-scale networks, we explore modeling of the local network via similar methods. We use a variety of link prediction algorithms and models, from simple to state-of-the-art, and compare their accuracy. Results of our experiments suggest that simple and easy to calculate prediction methods produce robust results, outperforming the more complicated state-of-the-art models we explored. We observe a variety of methods producing very accurate predictions, which suggests these methods can be effectively used to solve practical real-world problems associated with small local or intra-organizational networks.
Page Count
73
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2021
Copyright
Copyright 2021, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
ORCID ID
0000-0002-2510-4848
